Journal of Financial Market Infrastructures

Procyclicality of central counterparty margin models: systemic problems need systemic approaches

Pedro Gurrola-Perez

  • Initial margin models are inherently risk sensitive, which sets a limit to how much their procyclicality can be reduced without compromising the central counterparty's safety or the economic viability of central clearing.
  • We illustrate why this is the case by empirically testing the performance of standard initial margin models and of common anti-procyclicality tools during the March 2020 events and quantifying the different trade-offs involved.
  • The results highlight why the anti-procyclicality tools can only have limited success. In fact, some may lead to underestimating risks and, if a default happens, to potentially larger losses.
  • To move forward on this debate, it is essential to acknowledge that procyclicality is a systemic property and that, as such, it requires solutions that consider the whole system, identifying interdependencies and behaviors that could lead to negative feedback loops. There will be limited gain in having a minimally responsive initial margin model if other parts of the system are too sensitive to or inadequately prepared for a sharp increase in risk.

By definition, the market risk models used to estimate initial margin, whether for centrally cleared or bilaterally cleared trades, have to be sensitive to changes in market risk and, consequently, when market risk increases, initial margin requirements will tend to increase. To mitigate the potential effects of such procyclical behavior, central counterparties have different procyclicality mitigation tools put in place. However, there appeared to be renewed interest in the procyclicality of initial margin models after the market stress of March 2020. In this paper we argue that the focus on initial margin models is misplaced. First, margin calls are largely driven by variation margin, not initial margin. Second, the inherent risk sensitivity of margin models, the stochastic nature of the problem and the different trade-offs involved constrain what can be achieved with model calibration. We illustrate why this is the case by empirically testing the performance of standard initial margin models during the March 2020 events and quantifying the different trade-offs involved. If fragilities in the system therefore persist, how should these be addressed and who is responsible for addressing them? We argue that these questions demand a systemic perspective, focusing on the interactions between participants rather than on a single node.

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